Real-time FFB ripeness detection using IoT-enabled YOLOv8n  on Raspberry Pi 4 edge devices for precision agriculture

Nurul Hazlina Noordin; Rosdiyana Samad; Abdul Haikal Abdul Malek; Nurul Hazlina Noordin; Rosdiyana Samad; Abdul Haikal Abdul Malek

doi:10.55981/j.mev.2025.1220

Real-time FFB ripeness detection using IoT-enabled YOLOv8n on Raspberry Pi 4 edge devices for precision agriculture

Nurul Hazlina Noordin, Rosdiyana Samad, Abdul Haikal Abdul Malek

Abstract

This paper presents the development of an edge device for cost-effective implementation in agricultural environments. Experimental evaluations demonstrate accuracy and real-time performance, showcasing its potential for adoption in the industry. The proposed system provides a reliable tool for timely and accurate monitoring of fresh fruit bunch (FFB) ripeness, facilitating optimized crop management practices. The system employs the YOLOv8n model, renowned for its efficiency in real-time object detection tasks, and is adapted to run on the resource-constrained Raspberry Pi 4. To ensure seamless operation on edge devices, model optimization techniques such as quantization and hardware acceleration are implemented, enabling rapid decision-making based on live data feeds. A dataset comprising 4,194 annotated FFB images was utilized, with a [3,681:348:165] training-validation-test split. Performance evaluation demonstrated an average precision of 0.898 and a mean average precision (mAP) of 0.952. The system potentially enhances yield quality and sustainability while supporting data-driven decision-making in precision agriculture.

10.55981/j.mev.2025.1220

Keywords

edge devices; fresh fruit bunch ripeness detection; precision agriculture; Raspberry Pi 4; YOLOv8n object detection.

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References

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